Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns
Authors: Jing He, Xin Li, Lejian Liao, Dandan Song, William Cheung
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods. |
| Researcher Affiliation | Academia | 1BJ ER Center of HVLIP&CC, School of Comp. Sci., Beijing Institute of Technology, Beijing, China 2Department of Computer Science, Hong Kong Baptist University , Hong Kong, China {skyhejing, xinli, liaolj, sdd}@bit.edu.cn, william@comp.hkbu.edu.hk |
| Pseudocode | Yes | Algorithm 1 Our Proposed Methodology |
| Open Source Code | No | The paper does not provide any statement regarding the release of source code or a link to a code repository. |
| Open Datasets | Yes | We choose two large-scale datasets from real-world LBSNs, Foursquare and Gowalla, to conduct the experiments. Foursquare check-in data is within Los Angeles, provided by (Bao, Zheng, and Mokbel 2012), while Gowalla dataset is from (Cheng et al. 2012) with a complete snapshot. |
| Dataset Splits | No | The paper states: 'For other parameters, we tune them in the training sets to find the optimal values, and subsequently use them in the test set.' While this implies a form of validation for parameter tuning, it does not explicitly define a 'validation' dataset split with specific percentages or counts, or refer to a standard validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory, cloud services) used to run the experiments. |
| Software Dependencies | No | The paper describes the algorithms and models used (e.g., BPR, EM) but does not provide details on specific software dependencies, programming languages, or their version numbers used for implementation. |
| Experiment Setup | Yes | We set λΘ to be 1 for both FPMCLR and our proposed model. The empirical settings of the number of latent behavior patterns are 4 and 6 for Gowalla dataset and Foursquare dataset, respectively. For other parameters, we tune them in the training sets to find the optimal values, and subsequently use them in the test set. |